Firearms leave marks on both cartridge cases and bullets of the cartridges they fire. Comparing these specific marks on different bullets or cartridge cases, one can determine whether these bullets or cartridge cases are fired from the same firearm. Thus, the relations between the incidents where the firearms which fired these cartridge cases and bullets were used can be revealed.
In matching process, instruments called comparison microscopes that are specially designed and produced basically for this purpose are used. The cartridge case or bullet pairs to be analyzed are placed on the relevant part of this microscope and the intended image is acquired by using the microscopic capabilities of zooming in and out, rotating, shifting, adjusting light intensity and angle, developed for these purposes. The expert who analyzes the image can determine whether these two cartridge cases or bullets that are subjected to comparison are fired from the same firearm.
This visual comparison is a rather time-taking process for the expert. Although there are already developed products available, development of automated systems which help the expert by making at least certain preliminary eliminations in this time-taking process is a significant problem in the criminology literature. In the criminology literature, this problem is named automated firearms identification.
There are many steps in the solution of the problem of automated firearms identification. First of all, the required data should be properly digitalized. Accurate acquisition of the marks on the cartridge case and bullet is another problem in itself. Two-dimensional (2D) or three-dimensional (3D) information based systems are used in the solution of this problem. A summary of the literature on data acquisition problem are given in a resource (U. Sakarya, U. M. Leloglu, E. Tunah, “Three-dimensional surface reconstruction for cartridge cases using photometric stereo”, Forensic Science International, vol. 175, no. 2-3, pp. 209-217, 5 Mar. 2008). In order to find possible matches in the marks acquired, the exact feature which ensures success should be selected and this data should be promptly compared. The objective is to alleviate the expert's workload as far as possible by ensuring high identification success in an appropriate time period.
In a cartridge, the metal structure which contains the explosive and the bullet and which effectuates firing by the help of the primer (2) around it is called a cartridge case. A needlelike metal part of the firearm, called the firing pin, strikes the primer (2) igniting the igniter and the ignition explodes the explosive inside, and thus the cartridge is fired. In the meantime, under immense pressure and temperature, the cartridge case contacts the tray of the firearm under backward pressure. The immense pressure and temperature at the time of this contact results in sealing of the marks on the firearm tray against the cartridge case base (1) in accordance with the relevant physical process.
Cartridge cases fired from the same firearm is called sister cartridge cases. On the cartridge case base (1), basically three regions are investigated for the purpose of determination of sisterhood by ballistic image analysis. These are the primer (2), the ejector mark (3) and the firing pin mark (4). These regions are shown in FIG. 1.
Ejector is a name given to the mechanism that, after the ignition is effectuated and the bullet leaves the barrel, ejects the empty cartridge case out of the firearm. Although there are various mechanisms for ejection of the cartridge case out of the firearm, the most favorite method is that a pin called ejector hits the case wall and extracts it from the firearm. The ejector mark (3) left by the ejector pin on the cartridge case is one of the marks used in determination of sisters.
In determination of sisterhood by ballistic image analysis, the second region analyzed is the region of the firing pin mark (4). The marks left during the firing pin's entry into and exit out of the primer (2) are used in determination of the sister cartridge case.
Finally, the marks left on the cartridge case base (1) as a result of the high pressure created during firing are used in determination of the sister cartridge case. Here, as it is seen in FIG. 1, on the cartridge case base (1), presence of letters or special marks on the outside (the region between the circles no (1) and (2)) of the primer (2) area makes the analysis somewhat more difficult. Thus, in order to see the breech face marks, areas outside the firing pin mark (4) on the primer (2) are analyzed first. On the cartridge case base (1), presence of production-related marks (letters, prints, etc.), besides ballistic marks, somewhat complicates the matching process. Although certain types of cartridge cases have different shapes, cartridge cases in general have the structure shown in FIG. 1. The primer (2) is explicitly distinguished from the outer part of the cartridge case base (1). Furthermore, there are also certain marks and letters on the cartridge case base (1) outside the primer (2) area.
In the state of the art, certain regions on the cartridge case are segmented by the user while the cartridge case is recorded in a cartridge case data recording unit. For example, the BALISTIKA system developed by TÜBİTAK UZAY works this way. Automation of this segmentation process is a significant step especially for rapid data entry. In the current state of the art, furthermore, since letters and marks are present on the cartridge case base (1) on regions outside the primer (2), these regions are not generally used in the automatic matching process. Detection and elimination of these letters and marks, and usage of the remaining regions in the matching process may contribute positively to the automatic matching success.
Methods have been developed to make automatic cartridge case base (1) segmentation on the basis of 2D data. One of them is a method that operates in a system called Fireball (D. G. Li, “Image processing for the positive identification of forensic ballistics specimens”, Proceedings of the Sixth International Conference of Information Fusion, vol. 2, pp. 1494-1498, 2003): An edge map of the 2D cartridge case image is obtained by using the Canny edge detection method (J. Canny, “A Computational Approach to Edge Detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 679-698, 1986). On this edge map, the circles on the cartridge case base (1) are acquired by using the method of direct least squares fitting of ellipses (A. W. Fitzgibbon, M. Pilu and R. B. Fisher, “Direct least squares fitting of ellipses”, Proceedings of the 13th International Conference on Pattern Recognition, vol. 1, pp. 253-257, 25-29 Aug. 1996).
In another study, (J. Zhou, L. Xin, G. Rong ve D. Zhang, “Algorithm of automatic cartridge identification”, Optical Engineering, vol. 40, no. 12, pp. 2860-2865, 2001), first, the cartridge case base (1), the most outer circle, is found. The Hough transform method used for circle detection (L. G. Minor ve J. Sklansky, “Detection and segmentation of blobs in infrared images”, IEEE Trans. SMC, vol. 11, pp. 194-201, 1981) is used for cartridge case base (1) detection. The ejector mark (3) is found by a local frequency spectrum analysis made in the outer circle region. The active snake method (C. Xu and J. L. Prince, “Snakes, shapes, and gradient vector flow”, IEEE Trans. Image Process, vol. 7, no. 3, pp. 359-369, 1998) is used for the estimation of the firing pin mark (4) region.
In another study, in order to detect the circles and letters on the cartridge case base (1), Brein (C. Brein, “Segmentation of cartridge cases based on illumination and focus series”, Proceedings of SPIE, vol. 5685, Image and Video Communications and Processing 2005, Amir Said, John G. Apostolopoulos, Editors, pp. 228-238, March 2005) used image series under varied illumination conditions (point, ring, diffuse). Randomized Hough transform method (L. Xu, E. Oja and P. Kultanen, “A new curve detection method: Randomized Hough Transform (RHT)”, Pattern Recognition Letters, vol. 11, no. 5, pp. 331-338, 1990) was used for circle detection.
An automatic segmentation method on 3D cartridge case data was also developed (C. Brein, “Segmentation of cartridge cases based on illumination and focus series”, Proceedings of SPIE, vol. 5685, Image and Video Communications and Processing 2005, Amir Said, John G. Apostolopoulos, Editors, pp. 228-238, March 2005). 3D cartridge case data are acquired by the depth-from-focus method. Following the preprocessing carried out on the 3D data, the randomized Hough transform method (L. Xu, E. Oja and P. Kultanen, “A new curve detection method: Randomized Hough Transform (RHT)”, Pattern Recognition Letters, vol. 11, no. 5, pp. 331-338, 1990) is used to detect circles. Letters are also detected by using 3D data.